{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from transformers import VisionEncoderDecoderModel, AutoFeatureExtractor,AutoTokenizer\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "try:\n",
    "    nltk.data.find(\"tokenizers/punkt\")\n",
    "except (LookupError, OSError):\n",
    "    nltk.download(\"punkt\",quiet=True)\n",
    "\n",
    "\n",
    "from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor\n",
    "\n",
    "image_encoder_model = \"google/vit-base-patch16-224-in21k\"\n",
    "text_decode_model = \"gpt2\"\n",
    "\n",
    "model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(image_encoder_model, text_decode_model)\n",
    "# image feature extractor\n",
    "feature_extractor = AutoFeatureExtractor.from_pretrained(image_encoder_model)\n",
    "# text tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(text_decode_model)\n",
    "# GPT2 only has bos/eos tokens but not decoder_start/pad tokens\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "# update the model config\n",
    "model.config.eos_token_id = tokenizer.eos_token_id\n",
    "model.config.decoder_start_token_id = tokenizer.bos_token_id\n",
    "model.config.pad_token_id = tokenizer.pad_token_id\n",
    "output_dir = \"vit-gpt-model\"\n",
    "model.save_pretrained(output_dir)\n",
    "feature_extractor.save_pretrained(output_dir)\n",
    "tokenizer.save_pretrained(output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from torch.utils.data import Dataset\n",
    "from os import path\n",
    "import json\n",
    "\n",
    "class DeepFashionDataset(Dataset):\n",
    "    def __init__(self, data_path,max_target_length, train=True):\n",
    "        self.data_path = data_path\n",
    "        self.train = train\n",
    "        self.img_dir = path.join(data_path, \"images\")\n",
    "        self.max_target_length = max_target_length\n",
    "\n",
    "        if train:\n",
    "            json_dir = path.join(self.data_path,'train_captions.json')\n",
    "        else:\n",
    "            json_dir = path.join(self.data_path,'test_captions.json')\n",
    "\n",
    "        with open(json_dir, \"r\") as f:\n",
    "            data = json.load(f)\n",
    "        \n",
    "        self.images_name = list(data.keys())\n",
    "        self.labels = list(data.values())\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.images_name)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_name = self.images_name[idx]\n",
    "        img_path = path.join(self.img_dir, img_name)\n",
    "        model_inputs = dict()\n",
    "        model_inputs['labels'] = self.tokenization_fn(self.labels[idx], self.max_target_length)\n",
    "        model_inputs['pixel_values'] = self.feature_extraction_fn(img_path)\n",
    "        return model_inputs\n",
    "\n",
    "    # text preprocessing step\n",
    "    def tokenization_fn(self,captions, max_target_length):\n",
    "        \"\"\"Run tokenization on captions.\"\"\"\n",
    "        labels = tokenizer(captions, \n",
    "                        padding=\"max_length\", \n",
    "                        max_length=max_target_length,\n",
    "                        truncation=True).input_ids\n",
    "\n",
    "        return labels\n",
    "    \n",
    "    # image preprocessing step\n",
    "    def feature_extraction_fn(self, image_path):\n",
    "        \"\"\"\n",
    "        Run feature extraction on images\n",
    "        If `check_image` is `True`, the examples that fails during `Image.open()` will be caught and discarded.\n",
    "        Otherwise, an exception will be thrown.\n",
    "        \"\"\"\n",
    "        image = Image.open(image_path)\n",
    "\n",
    "        encoder_inputs = feature_extractor(images=image, return_tensors=\"np\")\n",
    "\n",
    "        return encoder_inputs.pixel_values[0]\n",
    "    \n",
    "train_ds = DeepFashionDataset('E:\\py_code\\img desctiption generate\\deepfashion-multimodal',max_target_length=30)\n",
    "eval_ds = DeepFashionDataset('E:\\py_code\\img desctiption generate\\deepfashion-multimodal', max_target_length=30,train=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 224, 224), (3, 224, 224))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds[0]['pixel_values'].shape,eval_ds[0]['pixel_values'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_ds[3]['labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10155, 2538)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_ds),len(eval_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
    "\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "    predict_with_generate=True,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    # per_device_train_batch_size=16,\n",
    "    # per_device_eval_batch_size=16,\n",
    "    output_dir=\"./image-captioning-output\",\n",
    "    auto_find_batch_size = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate\n",
    "metric = evaluate.load(\"rouge\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "ignore_pad_token_for_loss = True\n",
    "\n",
    "\n",
    "def postprocess_text(preds, labels):\n",
    "    preds = [pred.strip() for pred in preds]\n",
    "    labels = [label.strip() for label in labels]\n",
    "\n",
    "    # rougeLSum expects newline after each sentence\n",
    "    preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n",
    "    labels = [\"\\n\".join(nltk.sent_tokenize(label)) for label in labels]\n",
    "\n",
    "    return preds, labels\n",
    "\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    preds, labels = eval_preds\n",
    "    if isinstance(preds, tuple):\n",
    "        preds = preds[0]\n",
    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
    "    if ignore_pad_token_for_loss:\n",
    "        # Replace -100 in the labels as we can't decode them.\n",
    "        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    # Some simple post-processing\n",
    "    decoded_preds, decoded_labels = postprocess_text(decoded_preds,\n",
    "                                                     decoded_labels)\n",
    "\n",
    "    result = metric.compute(predictions=decoded_preds,\n",
    "                            references=decoded_labels,\n",
    "                            use_stemmer=True)\n",
    "    result = {k: round(v * 100, 4) for k, v in result.items()}\n",
    "    prediction_lens = [\n",
    "        np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds\n",
    "    ]\n",
    "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import default_data_collator\n",
    "\n",
    "# instantiate trainer\n",
    "trainer = Seq2SeqTrainer(\n",
    "    model=model,\n",
    "    tokenizer=feature_extractor,\n",
    "    args=training_args,\n",
    "    compute_metrics=compute_metrics,\n",
    "    train_dataset=train_ds,\n",
    "    eval_dataset=eval_ds,\n",
    "    data_collator=default_data_collator,\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"{'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': None, 'use_bfloat16': False, 'tf_legacy_loss': False, 'pruned_heads': {}, 'tie_word_embeddings': True, 'is_encoder_decoder': False, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'chunk_size_feed_forward': 0, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 'remove_invalid_values': False, 'exponential_decay_length_penalty': None, 'suppress_tokens': None, 'begin_suppress_tokens': None, 'architectures': ['ViTModel'], 'finetuning_task': None, 'id2label': {0: 'LABEL_0', 1: 'LABEL_1'}, 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, 'tokenizer_class': None, 'prefix': None, 'bos_token_id': None, 'pad_token_id': None, 'eos_token_id': None, 'sep_token_id': None, 'decoder_start_token_id': None, 'task_specific_params': None, 'problem_type': None, '_name_or_path': 'google/vit-base-patch16-224-in21k', 'hidden_size': 768, 'num_hidden_layers': 12, 'num_attention_heads': 12, 'intermediate_size': 3072, 'hidden_act': 'gelu', 'hidden_dropout_prob': 0.0, 'attention_probs_dropout_prob': 0.0, 'initializer_range': 0.02, 'layer_norm_eps': 1e-12, 'image_size': 224, 'patch_size': 16, 'num_channels': 3, 'qkv_bias': True, 'encoder_stride': 16, 'model_type': 'vit'}\" for key \"encoder\" as a parameter. MLflow's log_param() only accepts values no longer than 250 characters so we dropped this attribute. You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and avoid this message.\n",
      "Trainer is attempting to log a value of \"{'vocab_size': 50257, 'n_positions': 1024, 'n_embd': 768, 'n_layer': 12, 'n_head': 12, 'n_inner': None, 'activation_function': 'gelu_new', 'resid_pdrop': 0.1, 'embd_pdrop': 0.1, 'attn_pdrop': 0.1, 'layer_norm_epsilon': 1e-05, 'initializer_range': 0.02, 'summary_type': 'cls_index', 'summary_use_proj': True, 'summary_activation': None, 'summary_first_dropout': 0.1, 'summary_proj_to_labels': True, 'scale_attn_weights': True, 'use_cache': True, 'scale_attn_by_inverse_layer_idx': False, 'reorder_and_upcast_attn': False, 'bos_token_id': 50256, 'eos_token_id': 50256, 'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': None, 'use_bfloat16': False, 'tf_legacy_loss': False, 'pruned_heads': {}, 'tie_word_embeddings': True, 'is_encoder_decoder': False, 'is_decoder': True, 'cross_attention_hidden_size': None, 'add_cross_attention': True, 'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'chunk_size_feed_forward': 0, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 'remove_invalid_values': False, 'exponential_decay_length_penalty': None, 'suppress_tokens': None, 'begin_suppress_tokens': None, 'architectures': ['GPT2LMHeadModel'], 'finetuning_task': None, 'id2label': {0: 'LABEL_0', 1: 'LABEL_1'}, 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, 'tokenizer_class': None, 'prefix': None, 'pad_token_id': None, 'sep_token_id': None, 'decoder_start_token_id': None, 'task_specific_params': {'text-generation': {'do_sample': True, 'max_length': 50}}, 'problem_type': None, '_name_or_path': 'gpt2', 'n_ctx': 1024, 'model_type': 'gpt2'}\" for key \"decoder\" as a parameter. MLflow's log_param() only accepts values no longer than 250 characters so we dropped this attribute. You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and avoid this message.\n"
     ]
    },
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    {
     "ename": "RuntimeError",
     "evalue": "No executable batch size found, reached zero.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_5640\\4032920361.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrainer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\anaconda\\anaconda3\\lib\\site-packages\\transformers\\trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[0;32m   1535\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1536\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1537\u001b[1;33m             return inner_training_loop(\n\u001b[0m\u001b[0;32m   1538\u001b[0m                 \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1539\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda\\anaconda3\\lib\\site-packages\\accelerate\\utils\\memory.py\u001b[0m in \u001b[0;36mdecorator\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    132\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 134\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No executable batch size found, reached zero.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    135\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: No executable batch size found, reached zero."
     ]
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 74)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_ds[0]['pixel_values']),len(train_ds[0]['labels'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测推理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda\\anaconda3\\lib\\site-packages\\transformers\\models\\vit\\feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "image_captioner = pipeline(\"image-to-text\", model=\"./image-captioning-output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.image.AxesImage at 0x18c0cd307f0>,\n",
       " [{'generated_text': 'The sweater this woman wears has long sleeves and its fabric is cotton. The pattern of it is solid color. It has a v-shape neckline. This woman wears a three-point shorts, with cotton fabric and solid color patterns. The outer clothing is with cotton fabric and graphic patterns. There is an accessory on her wrist. This person is wearing a ring on her finger.'}])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt \n",
    "img_dir = r'E:\\py_code\\vitEnDe\\Image-captioning-ViT-main\\Image-Caption-Transformers\\th.jpg'\n",
    "a = image_captioner(img_dir,max_new_tokens = 100)\n",
    "img = Image.open(img_dir)\n",
    "plt.imshow(img),a"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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